The RPA software has not affected jobs so far at the bank because it is being used for one-off, time-consuming projects like mergers, Green said.

In banking, 70% of front-office jobs will be dislocated by AI, the researchers say: 485,000 tellers, 219,000 customer service representatives, and 174,000 loan interviewers and clerks.

For instance, banks that use AI software to generate suspicious activity reports will most likely create new jobs around explaining AI to regulators.

This is a question that has to go to the heart of every CEO of a large financial firm that hires AI software to replace human jobs, and it goes on the shoulders of the founders and developers that are building the software.

Chatbot backlash – A few banks have introduced chatbots to do work that might otherwise be done by customer service people.

But Boston-based startup Affectivathinks more needs to be done around the internal world of the carspecifically the emotional state of the driver.

Affectiva has built its business model around creating emotional AI, algorithms capable of recognizing human emotional states.

The company recently rolled out its first product, Affectiva Automotive AIa system capable of real-time analysis of the emotional states of drivers and passengers via cameras and voice recorders mounted into the cabin.

Speaking with Design News, Abdelrahamn Mahmoud, product manager at Affectiva, said that over the past year, the company’s technology has garnered a lot of interest from Tier 1 suppliers and OEMsparticularly in the automotive space.

In a talk at the recent 2018 GPU Technology Conference, Ashutosh Sanan, a computer vision scientist at Affectiva, explained the challenge around sensing emotion was in using temporal modelingmeaning the AI had to be able to discern emotions from sequences of images (i.e., video camera footage) rather than just a…

We analyzed the applications and value of three neural network techniques: – – For our use cases, we also considered two other techniquesgenerative adversarial networks (GANs) and reinforcement learningbut did not include them in our potential value assessment of AI, since they remain nascent techniques that are not yet widely…

Examples of where AI can be used to improve the performance of existing use cases include: – – In 69 percent of the use cases we studied, deep neural networks can be used to improve performance beyond that provided by other analytic techniques.

For the remaining 15 percent, artificial neural networks provided limited additional performance over other analytics techniques, among other reasons because of data limitations that made these cases unsuitable for deep learning (Exhibit 3).

On average, our use cases suggest that modern deep learning AI techniques have the potential to provide a boost in additional value above and beyond traditional analytics techniques ranging from 30 percent to 128 percent, depending on industry.

In many of our use cases, however, traditional analytics and machine learning techniques continue to underpin a large percentage of the value creation potential in industries including insurance, pharmaceuticals and medical products, and telecommunications, with the potential of AI limited in certain contexts.